Local and Global Convergence of an Inertial Version of Forward-Backward Splitting

Abstract: A problem of great interest in optimization is to minimize a sum of two closed, proper, and convex functions where one is smooth and the other has a computationally inexpensive proximal operator. In this paper we analyze a family of Inertial Forward-Backward Splitting (I-FBS) algorithms for solving this problem. We first apply a global Lyapunov analysis to I-FBS and prove weak convergence of the iterates to a minimizer in a real Hilbert space. We then show that the algorithms achieve local linear convergence for "sparse optimization", which is the important special case where the nonsmooth term is the $\ell_1$-norm. This result holds under either a restricted strong convexity or a strict complimentary condition and we do not require the objective to be strictly convex. For certain parameter choices we determine an upper bound on the number of iterations until the iterates are confined on a manifold containing the solution set and linear convergence holds. The local linear convergence result for sparse optimization holds for the Fast Iterative Shrinkage and Soft Thresholding Algorithm (FISTA) due to Beck and Teboulle which is a particular parameter choice for I-FBS. In spite of its optimal global objective function convergence rate, we show that FISTA is not optimal for sparse optimization with respect to the local convergence rate. We determine the locally optimal parameter choice for the I-FBS family. Finally we propose a method which inherits the excellent global rate of FISTA but also has excellent local rate.